2024-02-28
Do you ever make ‘’impulse purchases’’?
Why is the product always ‘’nearby’’ and ‘’easily available’’?
On some of the online retailer websites there is the ‘’wishlist’’ or ‘’shopping cart’’?
Recommendation systems have been based on subjective experience of marketers.
With online retailers machine learning has been used to learn the patterns of purchasing behavior.
With barcode scanners, computerized inventory systems, and online shopping there is a lot of transactional data available for data mining.
Do you know what an SKU is?
Answer: Stock Keeping Unit
In this chapter we will learn about methods for identifying associations among items in transactional data.
This is known as market basket analysis.
The result of market basket analysis is a set of association rules.
For example,
{peanut butter,jelly} -> {bread}Association rules are learned from subsets of itemsets.
Association rules were developed in the context of Big Data and database science and data mining for knowledge discovery (KDD).
Looking for the needle in the haystack.
Association rules are unsupervised, so there is no need for the algorithm to be trained.
And there is no objective measure of performance for such rule learners.
The complexity of transactional data is what makes association rule mining a challenging task.
Transactional datasets are typically extremely large, both in terms of the number of transactions and the number of features or items for sale.
The potential itemsets grows with the number of items for sale.
The good thing is that many itemsets are rare.
By igoring rare itemsets, it is possible to limit the search for rules.
The most widely used algorithm is the Apriori algorithm.
It employs a simple a priori belief as a guideline for reducing the association rule space, all subsets of a frequent itemset must also be frequent. This is the Apriori property.
See the paper
Fast algorithms for mining association rules, Agrawal and Srikant (1994).
Or
A comparison of association rule discovery and bayesian network causal inference algorithms, Bowes, et. al.
Whether or not an association rule is deemed interesting is determined by two statistical measures:
support
\(P(X)\)
confidence
\(P(Y|X)\)
By providing minimum thresholds for each of these metrics and applying the Apriori principle, it is easy to limit the number of rules reported.
The support of an itemset measures how frequently it occurs in the data.
\(support(X) = \frac{count(X)}{N}\)
where \(N\) is the number of transactions in the database and \(count(X)\) is the number of transactions that contain the itemset \(X\).
A rule’s confidence is a measurement of its predictive power or accuracy.
It is defined as the support of the itemset containing both \(X\) and \(Y\) divided by the support of the itemset containing only \(X\).
\(confidence(X \rightarrow Y) = \frac{support(X,Y)}{support(X)}\)
The confidence tells us the proportion of transactions where the presence of item or itemset \(X\) results in the presence of item or itemset \(Y\).
Note \(X \rightarrow Y\) is not the same as \(Y \rightarrow X\).
Rules that have high support and high confidence are referred to as strong rules.
The Apriori principle states that all subsets of a frequent itemset must also be frequent.
The Apriori algorithm uses the Apriori principle to exclude potential association rules prior to actually evaluating them.
The process of creating rules occurs in two phases:
The author gives an example of the use of Market Basket Analysis using transaction data to identify frequently purchased groceries with association rules.
Recommendation system
The example uses:
We will use the R packages
Lift is a metric used to measure how much more likely one item is to be purchased relative to its typical purchase rate, given that you know another item has been purchased.
\(lift(X \rightarrow Y) = \frac{confidence(X \rightarrow Y)}{support(Y)}\)
Here
\(lift(X \rightarrow Y) = lift(Y \rightarrow X)\)